Improving Triangle Geometry Shape Features Through Triangle Points Selection In Digit Recognition

Geometry features has been widely used in image processing especially in face recognition, fingerprint recognition, digit recognition, vehicle detection and also in intrusion. Among the commonly used geometry features are the features that are based on triangle properties. Generally, triangle proper...

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Main Author:	Arbain, Nur Atikah
Format:	Thesis
Language:	English English
Published:	2016
Subjects:	T Technology (General)
Online Access:	http://eprints.utem.edu.my/id/eprint/18357/1/Improving%20Triangle%20Geometry%20Shape%20Features%20Through%20Triangle%20Points%20Selection%20In%20Digit%20Recognition.pdf http://eprints.utem.edu.my/id/eprint/18357/2/Improving%20Triangle%20Geometry%20Shape%20Features%20Through%20Triangle%20Points%20Selection%20In%20Digit%20Recognition.pdf
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id	my-utem-ep.18357
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Azmi, Mohd Sanusi
topic	T Technology (General) T Technology (General)
spellingShingle	T Technology (General) T Technology (General) Arbain, Nur Atikah Improving Triangle Geometry Shape Features Through Triangle Points Selection In Digit Recognition
description	Geometry features has been widely used in image processing especially in face recognition, fingerprint recognition, digit recognition, vehicle detection and also in intrusion. Among the commonly used geometry features are the features that are based on triangle properties. Generally, triangle properties can be used to produce the features for image classification. To produce these features, triangle geometry need to be formed based on three coordinates which are the corners of A, B and C. However, not all triangle formations can be formed from the three coordinates due to the condition where corners of A, B and C may cause a straight line problem. The straight line problem occurs when the chosen coordinates of the corners of A, B and C are in a straight line which causes the triangle geometry impossible to be formed. On the other hand, the straight line occurs when the gradient of corners A, B and C produces the equivalent value. This can be proved by the experiment conducted to identify the gradient that has equivalent value where the position of coordinates A, B and C will determine either the triangle can be formed or vice versa. The purpose of this study is to suggest an improvement on triangle geometry shape through triangle point selection. To achieve this purpose, there are two objectives suggested for this study. They are: i) to propose straight line detection technique for corner A, B and C of triangle; and ii) to improve triangle shape by proposing location of corners based on dominant distribution of foreground image. In the experiment, four types of digit dataset are chosen which are IFCHDB, HODA, MNIST and BANGLA where each datasets is consisted of testing data and training data. The Detection of Triangle Point Selection (DTPS) is proposed to detect the triangle point that caused a straight line to be formed. Then, the straight line problem is solved using Triangle Geometry Based Dominant Distribution of Foreground Image (TD2FI). Next, the Triangle Features Based Summation of Gradient and Ratio (TSGR) and Enhancement of Proposed Triangle Features using Absolute Value (EFTA) are proposed in order to improve the classification accuracy result. The experimental results are yielded by comparing the results of classification accuracy between the present proposed methods with a prior proposed method using the supervised machine learning (SML). The SML used are the Support Vector Machine (SVM) and the Multi-Layer Perceptron (MLP). The result of classification accuracy has shown impressive results for TD2FI, TSGR and EFTA methods through the SVM and MLP techniques whereas the datasets from IFCHDB, HODA and BANGLA respectively have acquired good results through the SVM technique while MNIST dataset has acquired the highest result of classification accuracy through the MLP technique. The result of classification accuracy for TD2FI is 94.723% from IFCHDB dataset, 97.295% from HODA dataset, 95.4% from MNIST dataset and 90.275% from BANGLA dataset. In conclusion, the proposed method is capable of outstripping the straight line issue based on the position of the coordinates of corners A, B and C as well as produce a better result for classification accuracy.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Arbain, Nur Atikah
author_facet	Arbain, Nur Atikah
author_sort	Arbain, Nur Atikah
title	Improving Triangle Geometry Shape Features Through Triangle Points Selection In Digit Recognition
title_short	Improving Triangle Geometry Shape Features Through Triangle Points Selection In Digit Recognition
title_full	Improving Triangle Geometry Shape Features Through Triangle Points Selection In Digit Recognition
title_fullStr	Improving Triangle Geometry Shape Features Through Triangle Points Selection In Digit Recognition
title_full_unstemmed	Improving Triangle Geometry Shape Features Through Triangle Points Selection In Digit Recognition
title_sort	improving triangle geometry shape features through triangle points selection in digit recognition
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Information and Communication Technology
publishDate	2016
url	http://eprints.utem.edu.my/id/eprint/18357/1/Improving%20Triangle%20Geometry%20Shape%20Features%20Through%20Triangle%20Points%20Selection%20In%20Digit%20Recognition.pdf http://eprints.utem.edu.my/id/eprint/18357/2/Improving%20Triangle%20Geometry%20Shape%20Features%20Through%20Triangle%20Points%20Selection%20In%20Digit%20Recognition.pdf
_version_	1747833921293254656
spelling	my-utem-ep.183572021-10-10T15:35:19Z Improving Triangle Geometry Shape Features Through Triangle Points Selection In Digit Recognition 2016 Arbain, Nur Atikah T Technology (General) TA Engineering (General). Civil engineering (General) Geometry features has been widely used in image processing especially in face recognition, fingerprint recognition, digit recognition, vehicle detection and also in intrusion. Among the commonly used geometry features are the features that are based on triangle properties. Generally, triangle properties can be used to produce the features for image classification. To produce these features, triangle geometry need to be formed based on three coordinates which are the corners of A, B and C. However, not all triangle formations can be formed from the three coordinates due to the condition where corners of A, B and C may cause a straight line problem. The straight line problem occurs when the chosen coordinates of the corners of A, B and C are in a straight line which causes the triangle geometry impossible to be formed. On the other hand, the straight line occurs when the gradient of corners A, B and C produces the equivalent value. This can be proved by the experiment conducted to identify the gradient that has equivalent value where the position of coordinates A, B and C will determine either the triangle can be formed or vice versa. The purpose of this study is to suggest an improvement on triangle geometry shape through triangle point selection. To achieve this purpose, there are two objectives suggested for this study. They are: i) to propose straight line detection technique for corner A, B and C of triangle; and ii) to improve triangle shape by proposing location of corners based on dominant distribution of foreground image. In the experiment, four types of digit dataset are chosen which are IFCHDB, HODA, MNIST and BANGLA where each datasets is consisted of testing data and training data. The Detection of Triangle Point Selection (DTPS) is proposed to detect the triangle point that caused a straight line to be formed. Then, the straight line problem is solved using Triangle Geometry Based Dominant Distribution of Foreground Image (TD2FI). Next, the Triangle Features Based Summation of Gradient and Ratio (TSGR) and Enhancement of Proposed Triangle Features using Absolute Value (EFTA) are proposed in order to improve the classification accuracy result. The experimental results are yielded by comparing the results of classification accuracy between the present proposed methods with a prior proposed method using the supervised machine learning (SML). The SML used are the Support Vector Machine (SVM) and the Multi-Layer Perceptron (MLP). The result of classification accuracy has shown impressive results for TD2FI, TSGR and EFTA methods through the SVM and MLP techniques whereas the datasets from IFCHDB, HODA and BANGLA respectively have acquired good results through the SVM technique while MNIST dataset has acquired the highest result of classification accuracy through the MLP technique. The result of classification accuracy for TD2FI is 94.723% from IFCHDB dataset, 97.295% from HODA dataset, 95.4% from MNIST dataset and 90.275% from BANGLA dataset. In conclusion, the proposed method is capable of outstripping the straight line issue based on the position of the coordinates of corners A, B and C as well as produce a better result for classification accuracy. 2016 Thesis http://eprints.utem.edu.my/id/eprint/18357/ http://eprints.utem.edu.my/id/eprint/18357/1/Improving%20Triangle%20Geometry%20Shape%20Features%20Through%20Triangle%20Points%20Selection%20In%20Digit%20Recognition.pdf text en public http://eprints.utem.edu.my/id/eprint/18357/2/Improving%20Triangle%20Geometry%20Shape%20Features%20Through%20Triangle%20Points%20Selection%20In%20Digit%20Recognition.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=100238 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Information and Communication Technology Azmi, Mohd Sanusi 1. Aichert, A., 2008. Feature extraction techniques List of Figures. CAMP Medical Seminar WS0708, pp.1–8. 2. Alaei, A., Pal, U., and Nagabhushan, P., 2009. Using modified contour features and SVM based classifier for the recognition of Persian/Arabic handwritten numerals. Proceedings of the 7th International Conference on Advances in Pattern Recognition, ICAPR 2009, pp.391–394. 3. Alkhateeb, J.H., 2015. Off-Line Arabic Handwritten Isolated Character Recognition. International Journal of Engineering Science and Technology (IJEST), 7 (November), pp.251–257. 4. Alkhateeb, J.H., Ren, J., Jiang, J., and Al-Muhtaseb, H., 2011. Offline handwritten Arabic cursive text recognition using Hidden Markov Models and re-ranking. Pattern Recognition Letters, 32 (8), pp.1081–1088. 5. Arbain, N.A., Azmi, M.S., Melhem, L.B., Muda, A.K., and Rashaideh, H., 2016. Enhancement of Triangle Coordinates For Triangle Features For Better Classification. Jordanian Journal of Computers and Information Technology (JJCIT), 2 (2), pp.108–119. 6. Azmi, A.N., Nasien, D., and Shamsuddin, S.M., 2013a. A review on handwritten character and numeral recognition for Roman, Arabic, Chinese and Indian scripts. International Journal of advanced studies in Computers, Science & Engineering (IJASCSE), 2 (4), pp.1–8. 7. Azmi, M.S., Arbain, N.A., Muda, A.K., Abal Abas, Z., and Muslim, Z., 2015. Data Normalization for Triangle Features by Adapting Triangle Nature for better Classification. 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies, pp.1–4. 8. Azmi, M.S., Nasrudin, M.F., Omar, K., Ahmad, C.W.S.B.C.W., and Ghazali, K.W.M., 2013b. Exploiting features from triangle geometry for digit recognition. 2013 International Conference on Control, Decision and Information Technologies, CoDIT 2013, pp.876–880. 9. Azmi, M.S., Nasrudin, M.F., Omar, K., and Ghazali, K.W.M., 2012. Farsi/Arabic Digit Classification Using Triangle Based Model Features with Ranking Measures. 2012 International Conference on Image and Information Processing (ICIIP 2012), 46 (Iciip), pp.128–133. 10. Azmi, M.S. and Omar, K., 2013. Features Extraction of Arabic Calligraphy using extended Triangle Model for Digital Jawi Paleography Analysis. International Journal of Computer Information Systems and Industrial Management Applications, 5, pp.696–703. 11. Azmi, M.S., Omar, K., Nasrudin, M.F., Idrus, B., and Wan Mohd Ghazali, K., 2013c. Digit Recognition for Arabic/Jawi and Roman using Features from Triangle Geometry. AIP Conference Proceedings, 1522, pp.526–537. 12. Azmi, M.S., Omar, K., Nasrudin, M.F., and Muda, A.K., 2013d. Features Extraction of Arabic Calligraphy using extended Triangle Model for Digital Jawi Paleography Analysis. International Journal of Computer Information Systems and Industrial Management Applications, 5, pp.696–703. 13. Azmi, M.S., Omar, K., Nasrudin, M.F., Muda, A.K., and Abdullah, A., 2011a. Arabic Calligraphy Identification for Digital Jawi Paleography using Triangle Blocks. 2011 International Conference on Electrical Engineering and Informatics. 14. Azmi, M.S., Omar, K., Nasrudin, M.F., Muda, A.K., and Abdullah, A., 2011b. Arabic calligraphy classification using triangle model for Digital Jawi Paleography analysis. Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011, (July), pp.704–708. 15. Bar-Yosef, I., 2005. Input sensitive thresholding for ancient Hebrew manuscript. Pattern Recognition Letters, 26 (8), pp.1168–1173. 16. Bebis, G., Deaconu, T., and Georgiopoulos, M., 1999. Fingerprint identification using Delaunay triangulation. IEEE International Conference on Intelligence, Information, and Systems, pp.452–459. 17. Belaïd, A. and Choisy, C., 2008. Human reading based strategies for off-line Arabic word recognition. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4768 LNCS, pp.36–56. 18. Bhanu, B. and Tan, X., 2003. Fingerprint indexing based on novel features of minutiae triplets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25 (5), pp.616–622. 19. Bhattacharya, U. and Chaudhuri, B.B., 2009. Handwritten numeral databases of Indian scripts and multistage recognition of mixed numerals. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31 (3), pp.444–457. 20. Bhowmik, T.K., Bhattacharya, U., and Parui, S.K., 2004. Recognition of Bangla handwritten characters using an MLP classifier based on stroke features. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3316, pp.814–819. 21. Biglari, M., Mirzaei, F., and Neycharan, J.G., 2014. Persian/Arabic Handwritten Digit Recognition Using Local Binary Pattern. International Journal of Digital Information and Wireless Communications, 4 (4), pp.486–492. 22. Borji, A., Hamidi, M., and Mahmoudi, F., 2008. Robust handwritten character recognition with features inspired by visual ventral stream. Neural Processing Letters, 28 (2), pp.97–111. 23. Boukharouba, A. and Bennia, A., 2015. Novel feature extraction technique for the recognition of handwritten digits. Applied Computing and Informatics. 24. Brink, a. a., Smit, J., Bulacu, M.L., and Schomaker, L.R.B., 2012. Writer identification using directional ink-trace width measurements. Pattern Recognition, 45 (1), pp.162–171. 25. Bulacu, M. and Schomaker, L., 2007. Text-independent writer identification and verification using textural and allographic features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29 (4), pp.701–717. 26. Bulacu, M., Schomaker, L., and Brink, A., 2007. Text-independent writer identification and verification on offline arabic handwriting. Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 2, pp.769–773. 27. Chang, C.-C. and Lin, C.-J., 2011a. LIBSVM: A Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology, 2, pp.27:1--27:27. 28. Chang, C. and Lin, C., 2011b. LIBSVM : A Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2, pp.1–39. 29. Chaudhuri, B.B., 2006. A Complete Handwritten Numeral Database of Bangla – A Major India Script. 30. Chen, X., Tian, J., Yang, X., and Zhang, Y., 2006. An algorithm for distorted fingerprint matching based on local triangle feature set. IEEE Transactions on Information Forensics and Security, 1 (2), pp.169–177. 31. Chih-Wei Hsu, Chih-Chung Chang, and C.-J.L., 2008. A Practical Guide to Support Vector Classification. BJU international, 101 (1), pp.1396–400. 32. Cun, Y. Le, Boser, B., Denker, J.S., Henderson, D., R. E. Howard, W.H., and Jackel, L.D., 1990. Handwritten Digit Recognition with a Back-Propagation Network. Advances in Neural Information Processing Systems, pp.396–404. 33. Ebrahimpour, R., Esmkhani, A., and Faridi, S., 2010. Farsi handwritten digit recognition based on mixture of RBF experts. IEICE Electronics Express, 7 (14), pp.1014–1019. 34. Ebrahimpour, R., Moradian, M.R., Esmkhani, A., and Jafarlou, F.M., 2009. Recognition of Persian handwritten digits using Characterization Loci and Mixture of Experts. International Journal of Digital Content Technology and its Applications, 3 (3), pp.42–46. 35. Fan, R.-E., Chen, P.-H., and Lin, C.-J., 2005. Working Set Selection Using Second Order Information for Training Support Vector Machines. The Journal of Machine Learning Research, 6, pp.1889–1918. 36. Gago-Alonso, A., Hernández-Palancar, J., Rodríguez-Reina, E., and Muñoz-Briseño, A., 2013. Indexing and retrieving in fingerprint databases under structural distortions. Expert Systems with Applications, 40 (8), pp.2858–2871. 37. Gao, L. and Xu, Y., 2012. Face orientation recognition based on multiple facial feature triangles. Proceedings - 2012 International Conference on Control Engineering and Communication Technology, ICCECT 2012, pp.928–932. 38. Ghazvini, M., Sufikarimi, H., and Mohammadi, K., 2011. Fingerprint matching using genetic algorithm and triangle descriptors. 19th Iranian Conference on Electrical Engineering, pp.1–6. 39. Guo, X. and Cao, X., 2010. Triangle-constraint for finding more good features? Proceedings - International Conference on Pattern Recognition, pp.1393–1396. 40. Haselhoff, A. and Kummert, A., 2009. A Vehicle Detection System Based on Haar and Triangle Features. In: Intelligent Vehicles Sysmposium. pp.261–266. 41. Huang, Z. and Zhou, Y., 2010. Three-Dimensional Model Analysis and Processing. Advanced Topics in Science and Technology in China. Springer Berlin Heidelberg. 42. Jadied, E.M., 2015. Design and Implementation of Secure Fingerprint Template Using Key Binding Scheme and Random. 2015 3rd International Conference on Information and Communication Technology (ICoICT), pp.381–385. 43. Jain, A. and Prasad, M.V.N.K., 2015. Clustering Based Fingerprint Indexing Using Triangle Spiral. 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp.81–88. 44. Jakubowski, M. and Venkatesan, R., 2007. Randomized radon transforms for biometric authentication via fingerprint hashing. … of the 2007 ACM workshop on Digital …, pp.90–94. 45. Javidi, M.M. and Sharifizadeh, F., 2012. A Modified Decision Templates Method for Persian Handwritten Digit Recognition. Journal of American Science, 8 (1), pp.2012. 46. Jin, Z., Jin Teoh, A.B., Ong, T.S., and Tee, C., 2009. Secure minutiae-based fingerprint templates using random triangle hashing. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5857 LNCS, pp.521–531. 47. Kavati, I., Chenna, V., Prasad, M.V.N.K., and Bhagvati, C., 2014. Classification of Extended Delaunay Triangulation for Fingerprint Indexing. 2014 8th Asia Modelling Symposium, pp.153–158. 48. Khosravi, H. and Kabir, E., 2007. Introducing a very large dataset of handwritten Farsi digits and a study on their varieties. Pattern Recognition Letters, 28 (10), pp.1133–1141. 49. Knoll, A.L., 1969. Experiments with ‘Characteristic Loci’ for Recognition of Handprinted Characters. IEEE Transaction on Computers, 18 (April), pp.366–372. 50. Kussul, E. and Baidyk, T., 2004. Improved method of handwritten digit recognition tested on MNIST database. Image and Vision Computing, 22 (12 SPEC. ISS.), pp.971–981. 51. Labusch, K., Labusch, K., Barth, E., Barth, E., Martinetz, T., and Martinetz, T., 2008. Simple Method for High-Performance Digit Recognition Based on Sparse Coding. Neural Networks, IEEE Transactions on, 19 (11), pp.1985–1989. 52. Labusch, K., Labusch, K., Barth, E., Barth, E., Martinetz, T., Martinetz, T., Cruz, R.M.O., Cavalcanti, G., Ren, T., and Malik, J., 2010. Handwritten digit recognition using multiple feature extraction techniques and classifier ensemble. International Conference on Systems, Signals and Image Processing, 19 (11), pp.215–218. 53. Lai, C.H. and Suandi, S.A., 2011. Fingerprint Reference Point Detection Using Triangle ’ s Angle. 2011 IEEE Region 10 Conference, pp.354–358. 54. LeCun, Y.A., Bottou, L., Orr, G.B., and Müller, K.R., 2012. Efficient backprop. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7700 LECTU, pp.9–48. 55. LeCun, Y. and Cortes, C., 1992. MNIST handwritten digit database [online]. 56. Liu, C.-L., Nakashima, K., Sako, H., and Fujisawa, H., 2004. Handwritten digit recognition: investigation of normalization and feature extraction techniques. Pattern Recognition, 37 (2), pp.265–279. 57. Liu, C.L., Nakashima, K., Sako, H., and Fujisawa, H., 2003. Handwritten digit recognition: Benchmarking of state-of-the-art techniques. Pattern Recognition, 36 (10), pp.2271–2285. 58. Liu, Z.L.Z. and An, J.A.J., 2010. A new algorithm of global feature matching based on triangle regions for image registration. Signal Processing (ICSP), 2010 IEEE 10th International Conference on, pp.1248–1251. 59. Lorigo, L. and Gvindaraju, V., 2006. Offline Arabic Handwriting Recognition: A Survey. Pattern Analysis and Machine Intelligence, IEEE Transactions, pp.712–724. 60. Matan, O., Kiang, R.K., Stenard, C.E., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D., and Cun, Y. Le, 1990. Handwritten Character Recognition Using Neural Network Architectures. 4th USPS Advanced Technology Conference. 61. Mirsharif, G., Badami, M., Salehi, B., Azimifar, Z., and Collection, a D., 2012. Recognition of Farsi Handwritten Digits Using a Small Feature Set. International Journal of Computer and Electrical Engineering, 4 (4), pp.588–591. 62. Mohammad Masoud Javidi, 2011. Persian handwritten digits recognition: A divide and conquer approach based on mixture of MLP experts. International Journal of the Physical Sciences, 6 (30), pp.7007–7015. 63. M. S. Azmi, 2013. Fitur Baharu Dari Kombinasi Geometri Segitiga Dan Pengezonan Untuk Paleografi Jawi Digital. Doctoral dissertation, Universiti Kebangsaan Malaysia. 64. M. S. Azmi, K. Omar, M.F. Nasrudin, and A. Abdullah, 2012. The Novel Features for Digit Dataset using Triangle Based Model. The 2nd National Doctoral Seminar on Artificial Intelligence Technology 2012, pp.0–5. 65. Moradi, M., Pourmina, M.A., and Razzazi, F., 2010. FPGA-Based farsi handwritten digit recognition system. International Journal of Simulation: Systems, Science and Technology, 11 (2), pp.17–22. 66. Mozaffari, S., Faez, K., Faradji, F., Ziaratban, M., and A Golzan, S.M., 2006. A Comprehensive Isolated Farsi/Arabic Character Database for Handwritten OCR Research. Tenth International Workshop on Frontiers in Handwriting Recognition, pp.385–389. 67. Mozaffari, S., Faez, K., and M\"argner, V., 2007. Application of Fractal Theory for On-Line and Off-Line Farsi Digit Recognition. Machine Learning and Data Mining in Pattern Recognition, pp.868–882. 68. Page, J.D., 2015. Math Open Reference citations [online]. Available at: http://www.mathopenref.com/site/cite.html [Accessed 19 Nov 2015]. 69. Pan, W.M., Bui, T.D., and Suen, C.Y., 2009. Isolated handwritten Farsi numerals recognition using sparse and over-complete representations. Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, pp.586–590. 70. Parvez, M. and Mahmoud, S., 2013. Offline Arabic Handwritten Text Recognition: A Survey. ACM Computing Surveys, 45 (2), pp.23:1 – 23:34. 71. Plamondon, R. and Srihari, S.N., 2000. Online and off-line handwriting recognition: a comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (1), pp.63–84. 72. Qu, X., Xu, N., Wang, W., and Lu, K., 2015. Similar Handwritten Chinese Character Recognition based on Adaptive Discriminative Locality Alignment, (1), pp.2–5. 73. Rajabi, M., Nematbakhsh, N., and Amirhassan Monadjemi, S., 2012. A New Decision Tree for Recognition of Persian Handwritten Characters. International Journal of Computer Applications, 44 (6), pp.52–58. 74. Razali, N.M. and Wah, Y.B., 2011. Power comparisons of Shapiro-Wilk , Kolmogorov-Smirnov , Lilliefors and Anderson-Darling tests, 2 (1), pp.21–33. 75. Smith, P., Reid, D.B., Environment, C., Palo, L., Alto, P., and Smith, P.L., 1979. A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9 (1), pp.62–66. 76. Sundaresan, V. and Lin, J., 1998. Recognizing Handwritten Digits and Characters. 77. Tang, P., Jiang, R., and Zhao, M., 2010. Feature selection and design olintrusion detection system based on k-means and triangle area support vector machine. Second International Conference on Future Networks ICFN’10, pp.144–148. 78. Tin, M.M.M. and Sein, M.M., 2009. MULTI TRIANGLE BASED AUTOMATIC FACE RECOGNITION SYSTEM BY. In: I2MTC 2009 - International Instrumentation and Measurement Technology Conference. Singapore: IEE, pp.5–7. 79. Tin, M.M.M., Sein, M.M., and Township, H., 2009. Multi Triangle Based Automatic Face Recognition System By. I2MTC 2009 - International Instrumentation and Measurement Technology Conference, (May), pp.5–7. 80. Ujjwal Bhattacharya, Tanmoy Kanti Das, Amitava Datta, S.K.P.A.B.B.C., 2002. a Hybrid Scheme for Handprinted Numeral, 16 (7), pp.845–864. 81. Verma, B., Lu, J., Ghosh, M., and Ghosh, R., 2004. A feature extraction technique for online handwriting recognition. Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference, 2, pp.1337–1341. 82. Yang, W., Hu, J., Wang, S., and Yang, J., 2013. Cancelable Fingerprint Templates with Delaunay Triangle-Based Local Structures. Cyberspace Safety and Security, pp.81–91. 83. Zhang, Z. and Wang, S., 2012. Multi-feature facial synthesis based on triangle coordinate system. Proceedings of 2nd International Conference on Computer Science and Network Technology, ICCSNT 2012, (2), pp.141–145. 84. Zhang, Z., Wang, S., and Morphing, A.I., 2012. Multi-feature facial synthesis based on triangle coordinate system. Proceedings of 2nd International Conference on Computer Science and Network Technology, ICCSNT 2012, (2), pp.141–145. 85. Zheng, J., Gao, Y., and Zhang, M.-Z., 2009. Fingerprint Matching Algorithm Based on Similar Vector Triangle. In: Image and Signal Processing, 2009. CISP ’09. 2nd International Congress. Tianjin: IEEE, pp.1–6. 86. Zidouri, A., 2007. PCA-based Arabic Character feature extraction. 2007 9th International Symposium on Signal Processing and its Applications, ISSPA 2007, Proceedings, pp.20–23.

Improving Triangle Geometry Shape Features Through Triangle Points Selection In Digit Recognition

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